From Single-SNP to Wide-Locus: Genome-Wide Association Studies Identifying Functionally Related Genes and Intragenic Regions in Small Sample Studies

Background: Genome Wide Association Studies (GWAS) have had limited success when applied to complex diseases. Analyzing SNPs individually requires several large studies to integrate the often divergent results. In the presence of epistasis, multivariate approaches based on the linear model (including stepwise logistic regression) often have low sensitivity and generate an abundance of artifacts. Methods: Recent advances in distributed and parallel processing spurred methodological advances in non-parametric statistics. U-statistics for multivariate data (μStat) are not confounded by unrealistic assumptions (linearity, independence). Results: By incorporating knowledge about relationships between SNPs, μGWAS (GWAS based on μStat) can identify clusters of genes around biologically relevant pathways and pinpoint functionally relevant regions within these genes. Conclusion: With this computational biostatistics approach increasing power and guarding against artifacts, personalized medicine and comparative effectiveness will advance while subgroup analyses of Phase III trials can now suggest risk factors for adverse events and novel directions for drug development

Molecular, phenotypic, and sample-associated data to describe pluripotent stem cell lines and derivatives

The use of induced pluripotent stem cells (iPSC) derived from independent patients and sources holds considerable promise to improve the understanding of development and disease. However, optimized use of iPSC depends on our ability to develop methods to efficiently qualify cell lines and protocols, monitor genetic stability, and evaluate self-renewal and differentiation potential. To accomplish these goals, 57 stem cell lines from 10 laboratories were differentiated to 7 different states, resulting in 248 analyzed samples. Cell lines were differentiated and characterized at a central laboratory using standardized cell culture methodologies, protocols, and metadata descriptors. Stem cell and derived differentiated lines were characterized using RNA-seq, miRNA-seq, copy number arrays, DNA methylation arrays, flow cytometry, and molecular histology. All materials, including raw data, metadata, analysis and processing code, and methodological and provenance documentation are publicly available for re-use and interactive exploration at https://www.synapse.org/pcbc. The goal is to provide data that can improve our ability to robustly and reproducibly use human pluripotent stem cells to understand development and disease

Integrated Genomic Analysis of Diverse Induced Pluripotent Stem Cells from the Progenitor Cell Biology Consor tium

The rigorous characterization of distinct induced pluripotent stem cells (iPSC) derived from multiple reprogramming technologies, somatic sources, and donors is required to understand potential sources of variability and downstream potential. To achieve this goal, the Progenitor Cell Biology Consortium performed comprehensive experimental and genomic analyses of 58 iPSC from ten laboratories generated using a variety of reprogramming genes, vectors, and cells. Associated global molecular characterization studies identified functionally informative correlations in gene expression, DNA methylation, and/or copy-number variation among key developmental and oncogenic regulators as a result of donor, sex, line stability, reprogramming technology, and cell of origin. Furthermore, X-chromosome inactivation in PSC produced highly correlated differences in teratoma-lineage staining and regulator expression upon differentiation. All experimental results, and raw, processed, and metadata from these analyses, including powerful tools, are interactively accessible from a new online portal at https://www.synapse.org to serve as a reusable resource for the stem cell community

Multiple interactions between the alpha2C- and beta1-adrenergic receptors influence heart failure survival

<p>Abstract</p> <p>Background</p> <p>Persistent stimulation of cardiac β₁-adrenergic receptors by endogenous norepinephrine promotes heart failure progression. Polymorphisms of this gene are known to alter receptor function or expression, as are polymorphisms of the α_2C-adrenergic receptor, which regulates norepinephrine release from cardiac presynaptic nerves. The purpose of this study was to investigate possible synergistic effects of polymorphisms of these two intronless genes (<it>ADRB1 </it>and <it>ADRA2C</it>, respectively) on the risk of death/transplant in heart failure patients.</p> <p>Methods</p> <p>Sixteen sequence variations in <it>ADRA2C </it>and 17 sequence variations in <it>ADRB1 </it>were genotyped in a longitudinal study of 655 white heart failure patients. Eleven sequence variations in each gene were polymorphic in the heart failure cohort. Cox proportional hazards modeling was used to identify polymorphisms and potential intra- or intergenic interactions that influenced risk of death or cardiac transplant. A leave-one-out cross-validation method was utilized for internal validation.</p> <p>Results</p> <p>Three polymorphisms in <it>ADRA2C </it>and five polymorphisms in <it>ADRB1 </it>were involved in eight cross-validated epistatic interactions identifying several two-locus genotype classes with significant relative risks ranging from 3.02 to 9.23. There was no evidence of intragenic epistasis. Combining high risk genotype classes across epistatic pairs to take into account linkage disequilibrium, the relative risk of death or transplant was 3.35 (1.82, 6.18) relative to all other genotype classes.</p> <p>Conclusion</p> <p>Multiple polymorphisms act synergistically between the <it>ADRA2C </it>and <it>ADRB1 </it>genes to increase risk of death or cardiac transplant in heart failure patients.</p

Lampe1: An ENU-Germline Mutation Causing Spontaneous Hepatosteatosis Identified through Targeted Exon-Enrichment and Next-Generation Sequencing

Using a small scale ENU mutagenesis approach we identified a recessive germline mutant, designated Lampe1 that exhibited growth retardation and spontaneous hepatosteatosis. Low resolution mapping based on 20 intercrossed Lampe1 mice revealed linkage to a ∼14 Mb interval on the distal site of chromosome 11 containing a total of 285 genes. Exons and 50 bp flanking sequences within the critical region were enriched with sequence capture microarrays and subsequently analyzed by next-generation sequencing. Using this approach 98.1 percent of the targeted DNA was covered with a depth of 10 or more reads per nucleotide and 3 homozygote mutations were identified. Two mutations represented intronic nucleotide changes whereas one mutation affected a splice donor site in intron 11–12 of Palmitoyl Acetyl-coenzyme A oxygenase-1 (Acox1), causing skipping of exon 12. Phenotyping of Acox1Lampe1 mutants revealed a progression from hepatosteatosis to steatohepatitis, and ultimately hepatocellular carcinoma. The current approach provides a highly efficient and affordable method to identify causative mutations induced by ENU mutagenesis and animal models relevant to human pathology

Targeted Capture and Massively Parallel Sequencing in Pediatric Cardiomyopathy: Development of Novel Diagnostics

Pediatric cardiomyopathy is a genetically heterogeneous disease associated with significant morbidity. Although identification of underlying etiology is important for management, therapy, and screening of at risk family members, molecular diagnosis is difficult due to the large number of causative genes, the high rate of private mutations, and cost. In this study, we aimed to define the genetic basis of pediatric cardiomyopathy and test a novel diagnostic tool using a custom targeted microarray coupled to massively parallel sequencing. Three patients with cardiomyopathy were screened using a custom NimbleGen sequence capture array containing 110 genes and providing 99.9% coverage of the exons of interest. The sensitivity and specificity was over 99% as determined by comparison to long-range polymerase chain reaction (PCR)- based massively parallel sequencing, Sanger sequencing of missense variants, and single nucleotide polymorphisms genotyping using the Illumina Infinium Omni1 array. Overall, 99.73% of the targeted regions were captured and sequenced at over 10x coverage, allowing reliable mutation calling in all patients. Analysis identified a total of 165 non-synonymous coding single nucleotide polymorphisms (cSNPs) of which 89 were unique and 14 were novel. On average, each patient had 4 cSNPs predicted to be pathogenic. In conclusion, we report a cardiomyopathy sequencing array that allows simultaneous assessment of 110 genes. Comparison of targeted sequence capture versus PCR-based enrichment methods demonstrates that the former is more sensitive and efficient. Array-based sequence capture technology followed by massively parallel sequencing is promising as a robust and comprehensive tool for genetic screening of cardiomyopathy. These results provide important information about genetic architecture and indicate that improved annotation of variants and interpretation of clinical significance, particularly in cases with multiple rare variants, are important for clinical practice

Dense genotyping of immune-related disease regions identifies 14 new susceptibility loci for juvenile idiopathic arthritis

Analysis of the ImmunoChip single nucleotide polymorphism (SNP) array in 2816 individuals, comprising the most common subtypes (oligoarticular and RF negative polyarticular) of juvenile idiopathic arthritis (JIA) and 13056 controls strengthens the evidence for association to three known JIA-risk loci (HLA, PTPN22 and PTPN2) and has identified fourteen risk loci reaching genome-wide significance (p < 5 × 10(-8)) for the first time. Eleven additional novel regions showed suggestive evidence for association with JIA (p < 1 × 10(-6)). Dense-mapping of loci along with bioinformatic analysis has refined the association to one gene for eight regions, highlighting crucial pathways, including the IL-2 pathway, in JIA disease pathogenesis. The entire ImmunoChip loci, HLA region and the top 27 loci (p < 1 × 10(-6)) explain an estimated 18%, 13% and 6% risk of JIA, respectively. Analysis of the ImmunoChip dataset, the largest cohort of JIA cases investigated to date, provides new insight in understanding the genetic basis for this childhood autoimmune disease